6.2C Climate models (HL) Notes

Long-term Climate Data

1. Weather stations, observatories, satellites and radar systems provide decades to centuries of climate data.
2. Data types include air temperature, precipitation, pressure, wind speed, humidity and GHG concentrations.
3. Land-use change is monitored using aerial photography, satellite remote sensing and long-term vegetation and soil surveys.
4. Direct and indirect data sources allow scientists to reconstruct climate systems from both recent and ancient periods.

Importance of Long-Term Climate Data

1. Enables analysis of climate change and land-use change over decades to centuries.
2. Helps scientists identify long-term trends instead of short-term variability.
3. Essential for calibrating, validating and improving climate models.
4. Supports decision-making for mitigation and adaptation strategies.

Direct Measurements (Instrumental Records)

1. Collected using standardized scientific instruments in weather stations and observatories.
2. High accuracy and high temporal resolution (hourly, daily, monthly).
3. Provide consistent records since late 19th–early 20th century.

Key Direct Measurements

1. Temperature (thermometers, automated sensors).
2. Rainfall and humidity (rain gauges, hygrometers).
3. Atmospheric composition:
   1. Carbon dioxide (non-dispersive infrared sensors).
   2. Methane, nitrous oxide (gas chromatography).
4. Wind speed and direction (anemometers).
5. Atmospheric pressure (barometers).
6. Ocean temperature and salinity (buoys and ARGO floats).

Remote Sensing Measurements

1. Use satellites to record large-scale climate variables.
2. Provide global coverage, including remote oceans, mountains and polar regions.
3. Produce repeated measurements, creating multi-decade climate datasets.
   1. Passive sensors detect solar radiation reflected from Earth’s surface.
   2. Active sensors emit their own signal (e.g., radar) and detect reflections.

What Satellites Measure

1. Sea surface temperatures.
2. Sea-level rise (satellite altimetry).
3. Land-use change and deforestation.
4. Glacial mass and ice-sheet movement.
5. Aerosol concentration.
6. Cloud cover and thickness.
7. Thermal infrared radiation from Earth’s surface.

Indirect Measurements (Proxy Data)

1. Provide climatic information from hundreds to hundreds of thousands of years ago.
2. Critical because direct data only covers ~150 years.

1. Ice Cores

1. Contain trapped air bubbles storing ancient atmospheric gases.
2. Oxygen isotope ratios (¹⁶O vs ¹⁸O) indicate past temperature.
3. Dust layers reflect volcanic eruptions and land-use changes.
4. Higher CO₂ levels correspond to warmer climatic phases.

2. Dendrochronology (Tree-Ring Study)

1. Tree ring width indicates annual precipitation and temperature.
2. Wider rings represent wetter/warmer years
3. Narrow rings reflect drought or cold.
4. Growth interruptions signal fire, insect activity or land-use change.

3. Pollen from Peat Cores

1. Pollen preserved in peat layers indicates past vegetation.
2. Changes in pollen types reveal climate conditions and human land-use change.
3. Helps reconstruct historical ecosystems.

Role of Direct and Indirect Data in Climate Models

1. Direct data calibrates short-term trends.
2. Indirect data provides context over millennia.
3. Combined use improves climate model accuracy and future predictions.
4. Helps identify long-term patterns such as glacial cycles, mass extinction events, and industrial-era warming.

Global Climate Models (GCMs)

Purpose of Global Climate Models

1. Predict future climate conditions under different scenarios.
2. Represent physical processes of the atmosphere, oceans and land using equations.
3. Examine interactions among climate variables such as temperature, humidity, winds, salinity and aerosols.

Types of Climate Models

1. Energy Balance Models (EBMs): Represent the balance between incoming solar radiation and outgoing heat.
2. Radiative-Convective Models: Show energy transfer by radiation and convection in the vertical atmosphere.
3. General Circulation Models (GCMs): Simulate global atmospheric and oceanic circulation.
4. Coupled Models (AOGCMs): Link atmosphere, land, oceans and ice processes together.
5. Earth System Models (ESMs): Include biogeochemical cycles (carbon, nitrogen), vegetation changes and atmospheric chemistry.
6. Integrated Assessment Models (IAMs): Combine economics, land use, population and emissions patterns.

Model Structure

1. Earth’s surface divided into grid cells (horizontal and vertical).
2. Higher resolution (small cells) = more detail but greater computing power.
3. Coarse grids simplify Earth’s complexity, causing uncertainties.

Inputs (Forcings) Used in Climate Models

1. Solar radiation changes.
2. Greenhouse gas concentrations.
3. Aerosol levels.
4. Volcanic eruptions.
5. Land-use change.
6. Sea-ice extent and albedo.
7. Ocean salinity and temperature.

Why Inputs Are Uncertain

• Proxy data is incomplete in some regions.
• Human emissions vary depending on future policy.
• Aerosol behaviour is highly complex.
• Ocean heat uptake is difficult to measure accurately.

Outputs from Climate Models

1. Temperature projections.
2. Humidity and precipitation patterns.
3. Sea-level rise projections.
4. Glacial and ice-sheet melt estimates.
5. Ocean circulation changes.
6. Snow cover distribution.
7. Carbon cycle changes.

Hindcasting (Model Validation)

1. Runs models “backward” using present conditions to reproduce past climate.
2. If model output matches historical data → model is considered reliable.
3. If not, the model parameters must be adjusted.

What Hindcasting Compares

1. Past temperature trends.
2. Sea-level changes.
3. Ice sheet extent.
4. Carbon dioxide concentration trends.

Climate Models Using Different Scenarios

Why Scenarios Are Needed

1. Future climate depends on human choices.
2. Scenarios provide a range of possible futures based on emissions patterns.
3. Policymakers use them to make informed decisions.

Representative Concentration Pathways (RCPs)

1. RCP2.6: Low emissions → strong mitigation policies.
2. RCP4.5: Emissions peak mid-century, then decline.
3. RCP6.0: Medium stabilisation.
4. RCP8.5: High emissions → worst-case scenario.

Variables Predicted Under Different Scenarios

1. Sea-Level Rise: 0.26–1.08 m by 2100 depending on emissions.
2. Temperature Rise: 1.5–4°C depending on region and scenario.
3. Precipitation: Dry regions get drier; wet regions get wetter.
4. Extreme Weather: Frequency and intensity increase under high-emission scenarios.

Tipping Points and Critical Thresholds

1. A small change triggers a large, abrupt and often irreversible shift in the system.
2. Lead to a new equilibrium state.
3. Often associated with positive feedback loops.

Major Global Tipping Points

1. Antarctic Ice Sheet Melt

1. Rapid melt destabilizes ice shelves.
2. Sea-level rise accelerates beyond manageable levels.
3. Irreversible once certain thresholds are passed.

2. Slowing of Atlantic Thermohaline Circulation (AMOC)

1. Freshwater input from melting Greenland disrupts density-driven ocean flow.
2. Europe’s climate could cool significantly (up to 10–15°C in some regions).
3. Alters global weather patterns, monsoons and marine ecosystems.

3. Amazon Rainforest–Cerrado Transition

1. Deforestation reduces evapotranspiration, leading to less rainfall and hotter, drier conditions.
2. Forest fires increase due to drier conditions.
3. Loss of tree cover reduces carbon storage capacity.
4. Transition may occur if approximately 40% of forest is lost.

Positive Feedback Loops Leading to Tipping Points

1. Ice-albedo feedback accelerating Arctic and Antarctic melt.
2. Permafrost thaw releasing CO₂ and methane.
3. Loss of forest cover reducing rainfall → more fires → more forest loss.